(200j) Binary Variable Model for Chemical Supply Chain Optimization

In the chemical industrial operation, the quantity shortage will always occur in the chemical supply chain. This may cause local areas to suffer from tremendous economic losses and disorder industrial operation. Therefore dispatch optimization is very essential. In such urgent events of local shortage, agile dispatching through an effective transportation network, targeting the minimum recovery time, should be a top priority. The application of binary variable model can accurately identify the dispatch problem and guarantee the global optimization result. In this paper, a novel methodology is developed for network dispatch optimization under emergency of local shortage, which includes four stages of work.  First, emergency-area-centered network needs to be characterized, where the capacity, quantity, and availability of various sources are determined.  Second, the initial situation under emergency conditions needs to be identified.  Then, the optimization is conducted based on a developed MILP (mixed-integer linear programming) model in the third stage.  Finally, the sensitivity of the minimum dispatch time with respect to uncertainty parameters is characterized by partitioning the entire space of uncertainty parameters into multiple subspaces.  The efficacy of the developed methodology is demonstrated via a case study with in-depth discussions.